TY - EJOU
AU - Yang, Liming
AU - Bai, Junjian
AU - Sun, Qun
TI - Extreme Learning Machines Based on Least Absolute Deviation and Their Applications in Analysis Hard Rate of Licorice Seeds
T2 - Computer Modeling in Engineering \& Sciences
PY - 2015
VL - 108
IS - 1
SN - 1526-1506
AB - Extreme learning machine (ELM) has demonstrated great potential in machine learning and data mining fields owing to its simplicity, rapidity and good generalization performance. In this work, a general framework for ELM regression is first investigated based on least absolute deviation (LAD) estimation (called LADELM), and then we develop two regularized LADELM formulations with the l2-norm and l1-norm regularization, respectively. Moreover, the proposed models are posed as simple linear programming or quadratic programming problems. Furthermore, the proposed models are used directly to analyze the hard rate of licorice seeds using near-infrared spectroscopy data. Experimental results on eight different spectral regions show the feasibility and effectiveness of the proposed models.
KW - Extreme learning machine
KW - robust regression
KW - least absolute deviation estimation
KW - near-infrared spectroscopy
DO - 10.3970/cmes.2015.108.049